Operation monitoring method for treatment apparatus

ABSTRACT

In an operation monitoring method according to the present invention, operation data of a plasma processing system ( 1 ) are detected for every wafer (W) by means of a plurality of detectors, and a principal component analysis using the operation data is carried out by means of a controller ( 10 ). An operation state of the plasma processing system ( 1 ) is evaluated by using the results of the principal component analysis.

TECHNICAL FIELD

[0001] The present invention relates to a method for carrying out themonitoring of operation state, the evaluation of characteristics and soforth, in a processing system for, e.g. carrying out etching asemiconductor-wafer by means of plasma.

BACKGROUND ART

[0002] Semiconductor producing processes use various processing systems,such as semiconductor producing systems and inspection systems. Theseprocessing systems are designed to use various operation data to monitortheir operation states. If the operation states are considered to beabnormal, it is necessary to diagnose causes. When diagnosing thecauses, the various operation data are collected and analyzed forgrasping the operation states of the processing systems, to examine theplace of abnormality.

[0003] For example, a plasma processing system is used for etching,deposition or the like. For example, a plasma processing system of thistype comprises top and bottom electrodes which are provided in parallelin a processing vessel, and is designed to apply a high frequency powerto the bottom electrode and feed a process gas into the processingvessel to apply predetermined plasma-processing to an object to beprocessed, such as a semiconductor wafer. Then, the plasma processingsystem is designed to detect over thirty kinds of data, such as pressurein the processing vessel, voltage applied to the bottom electrode, andthe supply flow rate of the process gas, by means of detectors,respectively, to utilize the respective detected values as operationdata to monitor the operation state of the processing system.

[0004] However, if the processing system continues a predeterminedprocessing for a long time, the operation state varies with time, or theoperation state suddenly varies in some cases. In such cases,statistical data, such as mean, maximum, minimum and variance values,are separately obtained with respect to the operation data, such as thehigh frequency power, the flow rate of the process gas and the pressureof the process gas in the processing vessel, and the operation state ofthe processing system is evaluated on the basis of the respectivestatistical data. However, since the number of detectors is large, thereis a problem in that it is complicated and takes a lot of time to obtainthe statistical data with respect to the operation data of all thedetectors to evaluate the operation data every detector.

[0005] For example, when new processing systems or processing systemsafter maintenance are evaluated, the trial run of each of the processingsystems is carried out. Then, operation data obtained by the trial runare compared with operation data obtained by a corresponding detector ofa processing system as a reference (which will be hereinafter referredto as a “reference processing system”), one by one as shown in FIGS. 11through 15, and are analyzed. Therefore, there is a problem in that ittakes a lot of time to evaluate such a processing system.

[0006] Then, for example, the plasma processing system of this typeapplies a high frequency power to the electrodes in the processingvessel and feeds a process gas into the processing vessel to produceplasma of the process gas in the processing vessel to carry out apredetermined plasma processing with respect to a object to beprocessed, such as a semiconductor wafer. In this case, the object isprocessed after a high frequency source for supplying the high frequencypower is stabilized in accordance with the state in the processingvessel. However, immediately after the processing system is started, thehigh frequency source is unstable and is not stabilized for a long timeuntil the high frequency source adapts itself to the state in theprocessing vessel.

[0007] For example, FIG. 23a is a graph showing the variation of aparameter (voltage) relating to the high-frequency waves of a matchingcircuit, and FIG. 23b is a graph showing the variation of a parameter(capacitance) of a capacitor characterizing the matched state of thematching circuit. Both of the parameters vary with time, so that it isdifficult to determine the stable condition. For example, in theparameter shown in FIG. 23a, a peak appears at the beginning of a seriesof the wafers, but it is difficult to determine whether the parameter isstabilized. It also takes a lot of time until the inside of theprocessing vessel adapts itself to environment wherein the highfrequency power has been applied, and the inside of the processingvessel is not stable for a long time. Therefore, it is conventionallydetermined by operator's experience and intuition whether the states ofthe high frequency source and the inside of the processing vessel arestabilized. FIGS. 8a and 8 b show the results on an operating conditionon which the amount of deposition is small, after the processing vesselis evacuated for four days after the system is provided with maintenanceand inspected. This condition on which the amount of deposition is smallwill be described later.

[0008] Thus, there is no technique for objectively determining whetherthe high frequency source and inside of the processing vessel of theprocessing system are stabilized, and it must be relied on operator'sexperience and intuition. Since it is not possible to evaluateprocessing conditions for leading the processing system to its stablecondition, the evaluation must be relied on trial-and-error.

[0009] When the processing system is provided with maintenance andinspected, consumable goods are exchanged, and cleaning is carried out.However, since the processing system is a precision instrument, it isrequired to pay close attention to the assembly of the processingsystem. For example, even if there is slight loosening of screwing forthe respective parts of the high frequency source and in the processingvessel, or even if there are slight errors, such as errors in themounting of part of parts, plasma is unstable. However, conventionally,if the processing system is operated without finding such errors, thereis no technique for identifying the errors without opening andinspecting the processing system, so that it takes a lot of time todiagnose the causes.

DISCLOSURE OF THE INVENTION

[0010] The present invention has been made to solve the above describedproblems. It is an object of the present invention to provide a methodcapable of simply and surely carrying out the monitoring of an operationstate of a processing system and the evaluation of performance thereofby statistically integrating a large number of operation data into asmall number of data to analyze the integrated data.

[0011] In order to accomplish this object, according to the presentinvention, there is provided an operation monitoring method formonitoring an operation of a processing system by utilizing a pluralityof detected values as operation data, the detected values being detectedfor every object to be processed by means of a plurality of detectorsprovided in the processing system, wherein a multivariate analysis usingthe operation data is carried out to evaluate an operation state of theprocessing system.

[0012] There is also provided an operation monitoring method formonitoring an operation of a plasma processing system by utilizing aplurality of detected values as operation data, the detected valuesbeing detected for every object to be processed by means of a pluralityof detectors provided in the plasma processing system, the methodcomprising the steps of: obtaining previously a plurality of operationdata with respect to a plurality of objects to be processed asreferences; carrying out a principal component analysis using theoperation data; evaluating an operation state of the plasma processingsystem on the basis of results of the principal component analysis.

[0013] There is also provided n operation monitoring method formonitoring an operation of a processing system by utilizing a pluralityof detected values as operation data, the detected values being detectedfor every object to be processed by means of a plurality of detectorsprovided in the processing system, the method comprising the steps of:dividing the operation data into relatively high contribution principalcomponents and low contribution principal components; deriving aresidual matrix of operation data belonging to the low contributionprincipal components; and evaluating an operation state of theprocessing system on the basis of a residual score obtained by theresidual matrix.

[0014] There is also provided a processing system evaluating method forevaluating a difference in characteristics between a plurality ofprocessing systems by utilizing a plurality of detected values asoperation data, the detected values being detected for every object tobe processed by means of a plurality of detectors provided in eachprocessing system, the method comprising the steps of: obtaining firstoperation data for each of a plurality of objects to be processed byusing a reference processing system; carrying out a multivariateanalysis using the first operation data; obtaining second operation datafor each of the objects to be processed by using a comparativeprocessing system to be compared with the reference processing system;obtaining an analyzed result wherein the second operation data areadapted to results of the multivariate analysis; and comparing resultsof analysis based on the first operation data with the results ofanalysis based on the second operation data to evaluate a difference inperformance between the processing systems.

[0015] There is also provided a processing system evaluating method forevaluating a difference in characteristics between a plurality ofprocessing systems by utilizing a plurality of detected values asoperation data, the detected values being detected for every object tobe processed by means of a plurality of detectors provided in theprocessing systems, the method comprising the steps of: obtaining firstoperation data for each of the objects to be processed by using areference processing system; carrying out a principal component analysisusing the first operation data to derive a first residual matrix;obtaining second operation data for each of the objects to be processedby using a comparative processing system to be compared with thereference processing system; adapting the second operation data toresults of the principal component analysis to derive a second residualmatrix; and comparing the first residual matrix based on the firstoperation data with the second residual matrix based on the secondoperation data to evaluate a difference in performance between theprocessing systems.

[0016] It is another object of the present invention to provide anoperation monitoring method for a processing system capable ofoptimizing processing conditions to be operated by objectively decidinga stable condition after starting the processing system. It is a furtherobject of the present invention to provide an abnormality detectingmethod for a processing system capable of surely detecting abnormalitiesof the system without opening the system.

[0017] In order to accomplish this object, according to the presentinvention, there is provided a processing system monitoring method formeasuring a plurality of electrical data of a high frequency sourcevarying in accordance with a state in a processing system, by means of ameasuring device, when a high frequency power is applied to an electrodein a processing vessel from the high frequency source for processing anobject with plasma in the processing system, and for carrying out amultivariate analysis using the measured electrical data to detect apower application state of the high frequency source, the methodcomprising the steps of: measuring the electrical data as reference datawhen the power application state of the high frequency source isstabilized in accordance with the state in the processing vessel in areference processing system; carrying out a multivariate analysis forreference using the obtained reference data; measuring successively theelectrical data as comparative data in a comparative processing systemto be monitored, after the system is started; carrying out amultivariate analysis for comparison using the obtained comparativedata; and comparing a results of the multivariate analysis forcomparison with a result of the multivariate analysis for reference todetermine, on the basis of a difference therebetween, whether the powerapplication state of the high frequency source in the comparativeprocessing system reaches a stable condition in accordance with thestate in the processing vessel.

[0018] There is also provided an abnormality detecting method fordetecting an abnormality of a processing system by measuring a pluralityof electrical data of a high frequency source varying in accordance witha state in the processing system, by means of a measuring device, when ahigh frequency power is applied to an electrode in a processing vesselfrom the high frequency source for processing an object with plasma inthe processing system, and by carrying out a multivariate analysis usingthe measured electrical data to detect a power application state of thehigh frequency source, the method comprising the steps of: measuring theelectrical data as reference data when the power application state ofthe high frequency source is stabilized in accordance with the state inthe processing vessel in a normal reference processing system; carryingout a multivariate analysis for reference using the obtained referencedata; measuring the electrical data as comparative data in a comparativeprocessing system, the abnormality of which is to be detected; carryingout a multivariate analysis for comparison using the obtainedcomparative data; and comparing a result of the multivariate analysisfor comparison with a result of the multivariate analysis for referenceto detect the abnormality of the comparative processing system on thebasis of a difference therebetween.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019]FIG. 1 is a block diagram showing an example of a plasmaprocessing system to which an operation monitoring method according tothe present invention is applied;

[0020]FIG. 2 is a graph showing the variation in first principalcomponent score with respect to the plasma processing system shown inFIG. 1 obtained by a preferred embodiment of the present invention;

[0021]FIG. 3 is a graph showing the variation in high frequency voltagewith respect to the plasma processing system shown in FIG. 1;

[0022]FIG. 4 is a graph showing the variation in second principalcomponent score with respect to the plasma processing system shown inFIG. 1 obtained by a preferred embodiment of the present invention;

[0023]FIG. 5 is a graph showing the sudden variation in reflected waveof high frequency power relating to the second principal componentscores shown in FIG. 4;

[0024]FIG. 6 is a graph showing the sudden variation in high frequencyvoltage relating to the second principal component scores shown in FIG.4;

[0025]FIG. 7 is a graph showing the sudden variation in CO gas supplyquantity relating to the second principal component scores shown in FIG.4;

[0026]FIG. 8 is a graph showing a varying state of residual scores everywafer;

[0027]FIG. 9 is a graph showing residual scores of reference andcomparative processing systems obtained by a preferred embodiment of amethod for evaluating a processing system according to the presentinvention;

[0028]FIG. 10 is a graph showing residuals of parameters of acomparative processing system in which a residual scores are shiftedfrom those in a reference processing system, of the graph shown in FIG.9;

[0029]FIG. 11 is a graph showing the difference in parameter betweenprocessing systems used when the different points in performance betweenprocessing systems are conventionally compared and studied;

[0030]FIG. 12 is a graph corresponding to FIG. 11, which shows anotherparameter;

[0031]FIG. 13 is a graph corresponding to FIG. 11, which shows anotherparameter;

[0032]FIG. 14 is a graph corresponding to FIG. 11, which shows anotherparameter;

[0033]FIG. 15 is a graph corresponding to FIG. 11, which shows anotherparameter;

[0034]FIG. 16 is a block diagram showing an example of a processingsystem to which an operation monitoring method and abnormality detectingmethod according to the present invention are applied;

[0035]FIGS. 17a and 17 b are graphs showing transition until electricaldata of a processing system are stabilized by means of a high frequencymeasuring device, respectively;

[0036]FIGS. 18a and 18 b are graphs showing transition until residualscores of electrical data corresponding to FIGS. 17a and 17 b arestabilized, respectively;

[0037]FIGS. 19a and 19 b are graphs showing transition until electricaldata of a processing system are stabilized by means of a high frequencymeasuring device, respectively;

[0038]FIG. 20 is a graph showing transitions until residual scores ofelectrical data corresponding to FIGS. 17a and 17 b are stabilized, bystates A and B, respectively;

[0039]FIG. 21 is a graph showing residual scores based on electricaldata of normal and abnormal processing systems;

[0040]FIGS. 22a, 22 b and 22 c are graphs showing residual components ofelectrical data of abnormal processing systems, respectively; and

[0041]FIGS. 23a and 23 b are graphs showing the conventional variationin electrical data immediately after a processing system is started,respectively.

BEST MODE FOR CARRYING OUT THE INVENTION

[0042] The present invention will be described below on the basis ofpreferred embodiments shown in FIGS. 1 through 10 and 16 through 22 c.

[0043] First, referring to FIG. 1, an example of a plasma processingsystem to which a method according to the present invention is appliedwill be described. A plasma processing system 1 shown in FIG. 1comprises a processing vessel 11 of a conductive material, such asaluminum. In this processing vessel 11, a bottom electrode 12 alsoserving as a supporting table for supporting thereon a semiconductorwafer W serving as an object to be processed is provided on the bottomof the vessel. Above the bottom electrode 12, a top hollow groundedelectrode 13 also serving as a process gas supply part is provided so asto be a part from the bottom electrode 12. A magnetic field formingmeans 14 for applying a rotating magnetic field is provided so as tosurround the outer periphery of the processing vessel 11.

[0044] To the top face of the processing vessel 11, a gas supply pipe 15communicated with the top electrode 13 is connected. Thus, a process gasis supplied from a gas supply source (not shown) into the processingvessel 11 via the gas supply pipe 15 and top electrode 13. To the sideface of the processing vessel 11, a gas discharging pipe 16 connected toan evacuating unit (not shown) is connected. Thus, the pressure in theprocessing vessel 11 is reduced via the evacuating unit and gasdischarging pipe 16 so that the pressure in the processing vessel 11 isheld to be a predetermined degree of vacuum. A high frequency source 19is connected to the bottom electrode 12 so as to apply a high frequencypower to the bottom electrode 12 from the high frequency source 19.

[0045] The system 1 is designed to form high density plasma of a processgas in the processing vessel 11 by applying a rotating magnetic field Bdue to the magnetic field forming means 14, to an electric fieldproduced between the top and bottom electrodes 13 and 12 under thecontrol of a control unit 10. This plasma is intended to carry out auniform plasma processing, such as a predetermined etching, with respectto the wafer W in the processing vessel 11.

[0046] For example, 36 kinds of detectors are mounted on the plasmaprocessing system 1. By means of these detectors, for example, a highfrequency voltage V_(pp), a high frequency power P, a process gas flowrate F and so forth are sequentially detected as operation data during aplasma processing. These operation data are sequentially incorporatedinto the control unit 10, respectively. The control unit 10 storestherein, e.g. a principal component analysis program, as a multivariateanalysis program, to carry out a principal component analysis by thisprogram to monitor an operation state. That is, the control unit 10 isdesigned to monitor the operation state by evaluating the operationstate of the system by using operation data, which are the detectedvalues of the respective detectors, as parameters.

[0047] For example, when the principal component analysis is carried outin this preferred embodiment, a plurality of (e.g. 25) sample wafersserving as reference objects are previously etched. Then, the highfrequency voltage V_(pp), the high frequency power P, the process gasflow rate F and so forth are sequentially detected as operation data bymeans of the respective detectors every wafer to be processed. Thesedata are standardized through a centering by subtracting a mean valueand a scaling by dividing by a deviation every one of the voltage V_(pp)and other kinds of data. At this time, the correspondence between theoriginal operation data and standardized data has been clarified so asto correspond to, e.g. the sequence of data. For example, if n detectedvalues exist with respect to each of m wafers, a matrix includingstandardized operation data is expressed by mathematical expression (1).$\begin{matrix}{x = \begin{bmatrix}x_{11} & x_{12} & \cdots & \cdots & x_{1n} \\x_{21} & x_{22} & \cdots & \cdots & x_{2n} \\\cdots & \cdots & \cdots & \cdots & \cdots \\\cdots & \cdots & \cdots & \cdots & \cdots \\\cdots & \cdots & \cdots & \cdots & \cdots \\x_{m1} & x_{m2} & \cdots & \cdots & x_{mn}\end{bmatrix}} & (1)\end{matrix}$

[0048] Then, the control unit 10 calculates the mean, maximum, minimumand variance values of detected values every wafer. Then, avariance-covariance matrix based on these calculated values is used forcarrying out a principal component analysis for a plurality of operationdata to derive an eigenvalue and its eigenvector. The eigenvalue denotesthe magnitude of variance of operation data, and is defined as first,second . . . n-th principal components in order of the magnitude ofeigenvalue. Each eigenvalue has an eigenvector belonging thereto.Usually, as the degree n of a principal component is higher, thecontribution to the evaluation of data is lower, and its utility valueis lower.

[0049] For example, when n detected values are taken with respect toeach of m wafers, the j-th principal component corresponding to the j-theigenvalue of the i-th wafer is expressed by mathematical expression(2).

T _(ij) =x _(i1) P _(j1) +x _(i2) P _(j2) + . . . +x _(in) P _(jn)  (2)

[0050] Then, a value obtained by substituting the concrete i-th detectedvalues (x_(i1), x_(i2), . . . , x_(in)) for the j-th principal componentt_(ij) is a score of the j-th principal component of the i-th wafer.Therefore, the score t_(j) of the j-th principal component is defined bymathematical expression (3), and the eigenvector P_(j) of the j-thprincipal component is defined by mathematical expression (4).$\begin{matrix}{t_{j} = \begin{bmatrix}t_{ij} \\t_{2j} \\\cdots \\\cdots \\t_{mj}\end{bmatrix}} & (3) \\{P_{j} = \begin{bmatrix}P_{j1} \\P_{j2} \\\cdots \\\cdots \\P_{jn}\end{bmatrix}} & (4)\end{matrix}$

[0051] The t_(j) is a score denoting the relationship between measuredvalues, and P_(j) is an eigenvector denoting weights between measuredvalues. Using a matrix X and the eigenvector P_(j), the score t_(j) ofthe j-th principal component is expressed by mathematical expression(5).

T _(j) =XP _(j)  (5)

[0052] Using the scores of principal components and their eigenvectors,the matrix X is expressed by mathematical expression (6):

X=t ₁ P ₁ ^(T) +t ₂ P ₂ ^(T) + . . . +t _(n) P _(n) ^(T)  (6)

[0053] wherein P_(n) ^(T) is a transposed matrix of P_(n).

[0054] Therefore, even if various kinds of operation data exist, theprincipal component analysis can integrate them into a small number ofstatistical data of, e.g. the first and second principal components andthe third principal component at most, and evaluate and grasp anoperation state only by examining the small number of statistical data.For example, if the cumulative contribution of the eigenvalues of thefirst and second principal components exceeds 90%, the reliability ofevaluation based on the first and second principal components isgenerally high. The first principal component denotes a direction inwhich operation data are most greatly distributed (varied) as describedabove, to be an index for comprehensively evaluating the operation stateof the processing system, to be suitable for the decision and evaluationof the variation of the operation state of the processing system withtime. The second principal component is distributed in a directionperpendicular to the first principal component, to be an index of aninstantaneous shift from a normal operation state, to be suitable forthe decision and evaluation of the sudden variation in operation state.If the same kind of wafer is processed by means of the same processingsystem, the eigenvalues and their eigenvectors of the first and secondprincipal components are basically the same.

[0055] Therefore, in this preferred embodiment, a predeterminedprocessing system is used for processing a plurality of sample wafers onpredetermined conditions to derive eigenvalues and their eigenvectors onthe conditions. Then, these eigenvalues and their eigenvectors areapplied to actual wafers to decide and evaluate the operation state ofthe processing system during operation.

[0056] For example, in this preferred embodiment, the wafers are etchedon the under-mentioned conditions and, at the same time, the principalcomponent analysis of detected values of the respective detectors iscarried out. In this principal component analysis, a variance-covariancevalue is used for deriving eigenvalues. The maximum eigenvalue is afirst principal component having the maximum variance value. Theeigenvector of the first principal component is derived by using theeigenvalue and a variance-covariance value. Then, the first principalcomponent and a second principal component having the next magnitude areused for evaluating the operation state. If the first principalcomponent score t, is derived and recorded every wafer with respect to25 sample wafers when the processing system 10 is in a stable condition,corresponding to first through twenty-fifth wafers in FIG. 2. If thefirst principal component scores t₁ of 25 sample wafers are used forderiving a standard deviation σ of the first principal component scorest₁, all of the principal component scores t₁ of 25 wafers are within ±3σ. If the first principal component scores t₁ are within ±3 σ, theplasma processing system can be regarded as being operated in a normalstate.

[0057] [Processing Conditions]

[0058] Processing System: Magnetron RIE type Processing System

[0059] Wafer: 200 mm

[0060] Film to be etched: Silicon Oxide Film

[0061] Underlying Layer: Silicon Nitride Film

[0062] Processing Contents: SAC

[0063] Source High Frequency and

[0064] Power for Bottom Electrode: 13.56 MHz, 1700 W

[0065] Gap between Electrodes: 27 mm

[0066] Processing Pressure: 53 mTorr

[0067] Process Gas: C₄F₈=16 sccm, CO=300 sccm, Ar=400 sccm

[0068] Back Side Gas: He=7 Torr (Central Portion of Electrode),

[0069] 40 Torr (Edge Portion of Electrode)

[0070] Electrostatic Chuck DC Voltage: 1.5 KV

[0071] Processing Temperature: Top Electrode=60° C., Side Wall=60° C.,

[0072] Bottom Electrode=20° C. After the 25 sample wafers are used forcarrying out the principal component analysis as described above, wafersare actually etched on the same conditions, and operation data aredetected by the respective detectors. Then, the operation data of therespective detector and the eigenvector of the first principalcomponents obtained by the sample wafers are used for deriving a firstprincipal component score every wafer. These first principal componentscores are shown as those of the twenty-sixth wafer and thereafter. Itcan be seen from FIG. 2 that the first principal component scores belongto a normal operation range up to the one-hundred twentieth wafer,whereas the subsequent first principal component scores graduallydeviate from the normal operation range. It is considered that the causefor this is that plasma by-products adhere and accumulate in theprocessing vessel to gradually vary the operating conditions as thenumber of processed wafers increases.

[0073]FIG. 3 is a graph showing the variation of high frequency voltageV_(pp) with time during etching. It can be understood that the tendencyfor the,high frequency voltage V_(pp) to vary as shown in FIG. 3 is thesame as the tendency for the first principal component score to vary asshown in FIG. 2. From this, it can be understood that the firstprincipal component scores show the variation in operation state of theplasma processing system with time. Therefore, the operation ispreferably stopped to carry out maintenance and so forth in a goodtiming after the first principal component scores deviate from thenormal operation range.

[0074] As described above, according to this preferred embodiment, theoperation state is evaluated by carrying out the principal componentanalysis being a multivariate analysis with respect to operation datapreviously detected with respect to the sample wafers. Therefore, unlikea conventional method wherein the semantic contents of all of detectedvalues are individually compared and evaluated to observe the operationstate, the principal component analysis of a large number of operationdata can be automatically integrated into a small number of dataincluding the first and second principal component scores to simply andaccurately grasp the operation state even if the large number ofoperation data exist.

[0075] Since the first principal component scores in the principalcomponent analysis are used for an operation monitoring technique, thevariation in operation state with time can be grasped by the variationin first principal component score every wafer. By determining theshutdown time of the plasma processing system on the basis of a value±three times as large as the standard deviation σ of the first principalcomponent scores every wafer, the stop time of the plasma processingsystem, i.e. the maintenance time or the like, can be simply and surelygrasped.

[0076]FIG. 4 shows second principal component scores when sample wafersand actual wafers are etched. When the second principal component scoreof the actual wafers is derived, the eigenvector of the second principalcomponent obtained by the sample wafers is used. The variation of thesecond principal component scores is basically small, and the secondprincipal component scores always stably concentrate in a range nearzero. However, it can be seen that the second principal component scoressuddenly vary to greatly deviate from the operation range in places.Although the second principal component scores do not greatly varyimmediately after the start of etching, only one second principalcomponent score greatly varies after 40 wafers are processed, andrelatively many second principal component scores greatly vary after 120wafers are processed. Since the magnitude of variation is divided intothree groups, it is considered that the causes of variation arediffering from one group to another.

[0077] After the causes of variation for each group were examined, itwas found that the first group G1 in which the variation is greatest inFIG. 4 corresponds to the sudden variation in reflected wave of the highfrequency power in a range surrounded by ∘ in FIG. 5 (the axis ofabscissas in FIG. 5 shows the number of processed wafers in a lot anddoes not directly correspond to FIG. 4). It was found that the secondgroup G2 corresponds to the sudden variation of the high frequencyvoltage V_(pp) in a range surrounded by ∘ in FIG. 6 (the axis ofabscissas in FIG. 6 shows the time during the processing of a singlewafer). It was found that the third group G3 corresponds to the suddenvariation of the flow rate of CO gas of the process gases in a rangesurrounded by ∘ in FIG. 7 (the axis of abscissas in FIG. 7 shows thetime during the processing of a single wafer).

[0078] By thus using the second principal component scores every wafer,if a wafer having a suddenly varying second principal component score isfound, if only each of detected values of the wafer is verified, it ispossible to simply recognize that any one of the detected values isabnormal.

[0079] In this preferred embodiment, the variation in operation statewhich can not be sufficiently grasped by the first and second principalcomponents can be more surely grasped by the following technique. Forexample, if the cumulative contribution up to the k-th principalcomponent having a high contribution exceeds 90%, the variation inoperation state can be determined and evaluated by carrying out theprincipal component analysis, but the leakage of grasp may be 10% at themaximum. Therefore, a residual matrix E defined by mathematicalexpression (7) wherein lower contribution principal components of orderhigher than (k+1) are integrated into one is prepared (components ofrows correspond to detected values of detectors, respectively, andcomponents of columns correspond to the numbers of wafers,respectively). $\begin{matrix}{E = \begin{bmatrix}e_{11} & e_{12} & \cdots & \cdots & e_{1n} \\e_{21} & e_{22} & \cdots & \cdots & e_{2n} \\\cdots & \cdots & \cdots & \cdots & \cdots \\\cdots & \cdots & \cdots & \cdots & \cdots \\\cdots & \cdots & \cdots & \cdots & \cdots \\e_{m1} & e_{m2} & \cdots & \cdots & e_{mn}\end{bmatrix}} & (7)\end{matrix}$

[0080] If this residual matrix is adapted to the mathematical expression(6), the mathematical expression (6) is expressed by mathematicalexpression (8).

X=t ₁ P ₁ ^(T) +t ₂ P ₂ ^(T) + . . . +t _(k) P _(k) ^(T) +E  (8)

[0081] The residual score Q_(i) of this residual matrix E is defined bymathematical expression (10) using a row vector e_(i) defined bymathematical expression (9), and denotes residuals (errors) fromrespective detected values of the i-th wafer:

e _(i) =[e _(i1) e _(i2) . . . e _(in)]  (9)

Q _(i) =e _(i) e _(i) ^(T)  (10)

[0082] wherein e_(i) denotes the i-th measurement.

[0083] That is, the residual score Q_(i) is expressed as the product ofa row vector e_(i) and its transposed vector e_(i) ^(T), and is the sumof squares of the respective residuals so as to be capable of beingsurely derived as a residual without setting plus components off againstminus components.

[0084] By deriving this residual score Q, the operation state can bemultilaterally determined and evaluated. Since the eigenvectors of thefirst and second principal components are determined if only the firstand second principal components are used, the operation data (detectedvalues) of the respective detectors can not be multilaterally evaluated.On the other hand, the weight of the respective detected values servingas statistical data can be multilaterally evaluated by deriving theresidual matrix E, so that it is possible to grasp the variation inoperation state which can not be sufficiently grasped by the lower-orderof first to k-th components. Therefore, when the residual score Q_(i) ofa certain wafer deviates from the residual score Q₀ of the sample wafer,if the components of the row vector e_(i) is viewed, it is possible todetermine whether any one of detected values for the wafer greatlydeviates during the processing of the wafer, so that it is possible toidentify the cause of abnormality.

[0085] For example, if the cumulative contributions of the eigenvaluesof the first and second principal components exceed 90%, the first andsecond principal components can be used for determining the variation inoperation state with time and the sudden variation therein, and thevariation which can not be sufficiently grasped by the first and secondprincipal components can be grasped by the residual score Q_(i). If k=2,the first and second principal components and the residual matrix E areused, so that the mathematical expression (8) is expressed bymathematical expression (11).

X=t ₁ P ₁ ^(T) +t ₂ P ₂ ^(T) +E  (11)

[0086]FIG. 8 shows the recording of residual scores Q_(i) every waferwhen wafers are processed on the above described processing conditions.It can also be clearly seen from FIG. 8 that, similar to the firstprincipal components t_(i), the residual scores Q_(i) show a tendency tovary as the number of processed wafers increases. It can also be seenthat, similar to the second principal component scores, the residualscores Q_(i) suddenly vary. Thus, if the residual scores Q are grasped,phenomena capable of being grasped by the first and second principalcomponents can also be grasped, and phenomena capable of being notsufficiently grasped by the first and second principal components can bemultilaterally grasped. Then, if a parameter (operation data) having aparticularly larger residual than other parameters is noted in a rowcorresponding to a certain wafer in the residual matrix E, it ispossible to precisely verify which one of detected values for the wafersis abnormal.

[0087] As described above, when the respective scores of the first andsecond principal components are derived on the basis of operation data,operation data having a small residual other than these principalcomponents are integrated to be derived as a residual score Q. Thus, therespective detected values can be multilaterally grasped, and thevariation easy to fail to be noticed by the first and second principalscores can be surely grasped, so that the operation state can be graspedin detail. With respect to a wafer wherein abnormality has been found bythe residual scores Q, if the row components of the residual matrix Eare analyzed, it is possible to recognize which detected value of adetector is abnormal.

[0088] Second Preferred Embodiment

[0089] In the preceding preferred embodiment, there has been described amethod for evaluating an operation state by a principal componentanalysis when wafers are processed by a processing system. This methodcan also be used for determining and evaluating the individualdifference (the difference in characteristics, such as performance)between processing systems. That is, in this preferred embodiment, theresidual score Q is used for grasping the difference in characteristicsbetween processing systems. As described above, the residual score Q canbe used for multilaterally grasping the variation in each detected valueand for identifying the varying detected values.

[0090] For example, a reference processing system is first used forprocessing 25 wafers, and the detected values of a plurality ofdetectors are obtained as first operation data similar to the precedingpreferred embodiment. These first operation data are used as parametersfor carrying out a multivariate analysis to derive a residual matrix Eand its residual score Q₀. Then, characteristics of the referenceprocessing system are grasped on the basis of the residual score Q₀.Then, as described above, the value of the residual score Q₀ of thereference processing system is utilized as a reference value whencharacteristics of another processing system (a comparative processingsystem) to be compared therewith are determined and evaluated.

[0091] That is, after the residual score Q₀ of the reference processingsystem is obtained, the comparative processing system is used forprocessing wafers on the same conditions as those of the referenceprocessing system to obtain detected values of the respective detectorsas second operation data. Then, the second operation data obtained bythe comparative processing system are adapted to the above describedmathematical expression (11), which is previously used for obtaining theresidual score Q₀ for the reference processing system, so that theresidual score Q of the comparative processing system is derived. Next,the residual score Q of the comparative processing system is comparedwith the residual score Q₀ of the reference processing system to verifywhether the former value is coincident with the latter value. If theresidual score Q of the comparative processing system deviates from theresidual score Q₀ of the reference processing system, it can be seenthat at least one of detected values of the comparative processingsystem deviates from the reference value. In this preferred embodiment,rows of the residual matrix E comprises residuals of the respectivedetectors every wafer in the processing system.

[0092] In this preferred embodiment, as shown in FIG. 9, systems F and Iare used as reference processing systems for etching 25 wafers on thesame conditions as those in the preceding preferred embodiment. Then,first operation data being detected values of the respective detectorsof the reference processing systems F and I are used as parameters forcarrying out a principal component analysis similar to the precedingpreferred embodiment to derive eigenvalues and eigenvectors of first andsecond principal components and to derive a residual score Q. Then,constants, such as the eigenvalues and eigenvectors obtained by theprincipal component analysis with respect to the reference processingsystems F and I are set in a main principal analysis program forcomparative processing systems A to E, G, H and J. Then, the comparativeprocessing systems A to E, G, H and J are used for etching wafers on thesame conditions, and detected values of the respective detectors areobtained as second operation data. The results of residual scores Qderived every processing system are shown in FIG. 9. In order to obtainthe residual scores Q, the above described mathematical expression (10)is used.

[0093] According to the results shown in FIG. 9, the residual scores ofthe processing systems A, D, G and J are hardly different from theresidual scores Q of the reference processing systems F and I, whereasthe residual scores Q of the processing systems B, C, E and H greatlydeviate from the residual scores Q of the reference processing systems.Therefore, it can be seen that the residuals of at least one ofparameters of the processing systems B, C, E and H greatly vary fromthose of the reference processing systems F and I. Therefore, in orderto study the parameter having the large residual, FIG. 10 showingresiduals of the respective parameters of the processing systems B, C, Eand E is viewed. Then, it was found that the residuals of parameters G,H and K are large in the processing system B, the residuals ofparameters C, H, J and K are large in the processing system C, theresiduals of parameters C and H are large in the processing system E,and the residuals of parameters G, H and J are large in the processingsystem H. Thus, when the residual score Q of the comparative processingsystem greatly deviates from the residual scores Q of the referenceprocessing systems F and I, if the residuals of parameters with respectto the respective detectors of the processing system are compared, it ispossible to simply identify a detector causing the deviation.

[0094] As described above, if only the residual score Q of thecomparative processing system is derived to be compared with theresidual score Q₀ of the reference processing system, it is possible tosimply evaluate a comparative processing system having characteristicsdeviating from those of the reference processing system. The residualsof the respective parameters of the comparative processing system can befound at a glance as shown in FIG. 10. If a specific parameter having alarge residual is recognized, it is possible to simply fine that theparameter deviates from that of the reference processing system.Therefore, when the performance of a newly produced processing system ora processing system after maintenance is adjusted, it is possible tosimply fine malfunction on performance only by deriving a residualmatrix E and its residual score Q of the processing system, and it ispossible to identify the specific malfunction, so that it is possible toadjust performance in a short time.

[0095] The principal component analysis using the variance-covariancehas been described above. Since the detected values of a plurality ofdetectors have inherent units, respectively, if the respective detectedvalues are used as data for the principal component analysis as theyare, there are some cases where it is not possible to carry outevaluation precisely reflecting the operation data. Therefore, in orderto precisely evaluate the operation state, excluding the influence ofthe difference in unit between the respective detected values, all ofdetected value data are stabilized and a principal component analysisusing a correlation matrix is carried out.

[0096] In this preferred embodiment, the residual score Q can be usedfor grasping the difference in characteristics, e.g. the difference inperformance, between the processing systems, to evaluate whether theperformance of the processing system is relatively good or bad. If theresidual component is viewed, it is possible to simply and rapidlyidentify a portion having lower performance. Therefore, it is possibleto simply and rapidly carry out the determination and evaluation ofperformance of a newly produced processing system or a processing systemafter maintenance.

[0097] Incidentally, in the above described preferred embodiment, thesame effects can be obtained if the above described processingconditions are changed into the following processing conditions 1through 5.

[0098] [Processing Conditions 1]

[0099] Processing System: Magnetron RIE type Processing System

[0100] Wafer: 300 mm

[0101] Film to be etched: Silicon Oxide Film

[0102] Underlying Layer: Si

[0103] Processing Contents: Contact Hole

[0104] Bottom Electrode:

[0105] Source High Frequency=13.56 MHz, Source Power: 4000 W

[0106] Gap between Electrodes: 40 mm

[0107] Processing Pressure: 40 mTorr

[0108] Process Gas: C₄F₈=20 sccm, CO=100 sccm, Ar=440 sccm,

[0109] O₂=10 sccm

[0110] Back Side Gas: He=10 Torr (Central Portion of Electrode),

[0111] 50 Torr (Edge Portion of Electrode)

[0112] Electrostatic Chuck DC Voltage: 2.5 KV

[0113] Processing temperature: Top Electrode 60° C., Side Wall=60° C.,

[0114] Bottom Electrode=10° C.

[0115] [Processing Conditions 2]

[0116] Processing System: Magnetron RIE type Processing System

[0117] Wafer: 300 mm

[0118] Film to be etched: Silicon Oxide Film

[0119] Underlying Layer: SiN

[0120] Processing Contents: SAC

[0121] Bottom Electrode:

[0122] Source High Frequency=13.56 MHz, Source Power: 4000 W

[0123] Gap between Electrodes: 40 mm

[0124] Processing Pressure: 40 mTorr

[0125] Process Gas: C₄F₈=24 sccm, CO=450 sccm, Ar=600 sccm

[0126] Back Side Gas: He=10 Torr (Central Portion of Electrode),

[0127] 50 Torr (Edge Portion of Electrode)

[0128] Electrostatic Chuck DC Voltage: 2.5 KV

[0129] Processing temperature: Top Electrode 60° C., Side Wall=60° C.,

[0130] Bottom Electrode=10° C.

[0131] [Processing Conditions 3]

[0132] Processing System: Double Channel Plasma Etching System

[0133] (Voltage is applied to Both of Top and Bottom Electrodes)

[0134] Wafer: 300 mm

[0135] Film to be etched: Silicon Oxide Film

[0136] Underlying Layer: Si, Metal Film

[0137] Processing Contents: Through Hole, Via Contact

[0138] Top Electrode:

[0139] Source Frequency=60 MHz, Source Power: 3300 W

[0140] Bottom Electrode:

[0141] Source High Frequency=2 MHz, Source Power: 3800 W

[0142] Gap between Electrodes: 35 mm

[0143] Processing Pressure: 25 mTorr

[0144] Process Gas: C₅F₈=32 sccm, Ar=750 sccm, O₂=45 sccm

[0145] Back Side Gas: He=20 Torr (Central Portion of Electrode),

[0146] 35 Torr (Edge Portion of Electrode)

[0147] Electrostatic Chuck DC Voltage: 2.5 KV

[0148] Processing Temperature: Top Electrode 60° C., Side Wall=50° C.,

[0149] Bottom Electrode=20° C.

[0150] [Processing Conditions 4]

[0151] Processing System: Double Channel Plasma Etching System (Voltageis applied to Both of Top and Bottom Electrodes)

[0152] Wafer: 300 mm

[0153] Film to be etched: Polysilicon

[0154] Underlying Layer: Thermal Oxide Film

[0155] Processing Contents: Gate

[0156] Top Electrode:

[0157] Source Frequency=60 MHz, Source Power: 200 W

[0158] Bottom Electrode:

[0159] Source High Frequency=13.56 MHz, Source Power: 150 W

[0160] Gap between Electrodes: 170 mm

[0161] Processing Pressure: 30 mTorr

[0162] Process Gas: HBr=400 sccm

[0163] Back Side Gas: He=3 Torr (Central Portion of Electrode),

[0164] 3 Torr (Edge Portion of Electrode)

[0165] Electrostatic Chuck DC Voltage: 3.0 KV

[0166] Processing temperature: Top Electrode 80° C., Side Wall=60° C.,

[0167] Bottom Electrode=60° C.

[0168] [Processing Conditions 5]

[0169] Processing System: Double Channel Plasma Etching System (Voltageis applied to Both of Top and Bottom Electrode)

[0170] Wafer: 300 mm

[0171] Film to be etched: Si

[0172] Underlying Layer: −

[0173] Processing Contents: ST1

[0174] Top Electrode:

[0175] Source Frequency=60 MHz, Source Power: 1800 W

[0176] Bottom Electrode:

[0177] Source High Frequency=13.56 MHz, Source Power: 300 W

[0178] Gap between Electrodes: 170 mm

[0179] Processing Pressure: 100 mTorr

[0180] Process Gas: O₂=5 sccm, HBr=570 sccm, Cl₂=30 sccm,

[0181] Back Side Gas: He='Torr (Central Portion of Electrode),

[0182] 3 Torr (Edge Portion of Electrode)

[0183] Electrostatic Chuck DC Voltage: 3.0 KV

[0184] Processing Temperature: Top Electrode 80° C., Side Wall=60° C.,

[0185] Bottom Electrode=60° C.

[0186] Third Preferred Embodiment

[0187] A preferred embodiment relating to electrical data of a highfrequency source in a processing system will be described below.

[0188] First, referring to FIG. 16, an example of a processing system towhich this preferred embodiment is applied will be described. In aprocessing system 1′ shown in FIG. 16, the same reference numbers aregive to components which are substantially the same as those in theprocessing system 1 shown in FIG. 1, and the detailed descriptionsthereof are omitted. The processing system 1′ shown in FIG. 16 comprisesa processing vessel 11 of a conductive material, such as aluminum. Inthis processing system 1′, the grounded top face 11 a of the processingvessel 11 serves as a top electrode facing a bottom electrode 12 alsoserving as a supporting table. The processing system 1′ is designed toform a high density plasma of a process gas fed into the processingvessel 11, by applying a rotating magnetic field B due to a magneticfield forming means 14, to an electric field produced between the topand bottom electrodes 11 a and 12 under the control of a control unit10′. This plasma is intended to carry out a uniform plasma processing,such as a predetermined etching, with respect to a wafer W in theprocessing vessel 11. On a periphery of the bottom electrode 12, a focusring 20 is arranged for causing the plasma to converge on the wafer W.

[0189] In this preferred embodiment, a matching circuit 18 and a highfrequency measuring device 17 are sequentially provided between a highfrequency source 19 and the bottom electrode 12. A high frequency powerof 13.56 MHz is applied to the bottom electrode 12 from the highfrequency source 19. In this case, a higher harmonic wave (e.g. 27.12MHz, 40.68 MHz) based on a high frequency 13.56 MHz as a fundamentalwave is also applied to the electrode 12. However, electrical data, suchas the voltage, current, phase and impedance, of the high frequencypower applied to the bottom electrode 12 from the high frequency source19 are unstable immediately after the starting of the system 1′, and arenot easily stabilized. There is no technique for directly recognizingthe state in the processing vessel 1. Therefore, in this preferredembodiment, these electrical data, such as voltage, current, phase andimpedance, are measured, and the respective measured values are utilizedfor detecting the stable condition of the processing system 1′,specifically the stable condition required to carry out a predeterminedplasma processing in the processing vessel 11.

[0190] That is, the high frequency measuring device 17 is used forintermittently measuring voltage, current, phase and impedance aselectrical data of a fundamental wave of the high frequency source 19and its higher harmonic waves until the high frequency source 19 isstabilized after the processing system 1′ is started, and theseelectrical data are sequentially incorporated into the control unit 10′.The control unit 10′ stores therein a principal component analysisprogram as a multivariate analysis program to carry out the principalcomponent analysis of measured values by means of the analysis programto detect the stable condition of the processing system.

[0191] For example, when the principal component analysis is carried outin this preferred embodiment, a reference processing system in a statethat voltage applied to the electrode 12 from the high frequency source19 has been stable is used for intermittently measuring voltage V,current I, phase P and impedance Z as reference data, respectively.These data V, I, P and Z are electrical data of the fundamental wave ofthe high frequency source 19 and its higher harmonic waves. Thus,measured values V(f_(n)), I(f_(n)), P(f_(n)) and Z(f_(n)) serving asreference data of the respective frequencies f_(n) are obtained. Thesemeasured values are standardized through a centering by subtracting amean value and a scaling by dividing by a deviation every one of thevoltage V and other kinds of values. At this time, the correspondencebetween the original measured values and standardized measured valueshas been clarified by arranging them in accordance with, e.g. thesequence of measured values. Then, assuming that the number of variousstandardized measured values is n and that m measurements are carriedout (the number of wafers is m) until stabilized, a matrix including allof standardized measured values as reference data of the referenceprocessing system is expressed by the above described mathematicalexpression (1).

[0192] Then, the control unit 10′ derives the mean, maximum, minimum andvariance values of all of the stabilized measured values, and avariance-covariance matrix based on these calculated values is used forcarrying out a principal component analysis for the stabilized measuredvalues to derive an eigenvalue and its eigenvector.

[0193] For example, n standardized measured values are taken in each ofm measurements, and the j-th principal component corresponding to thej-th eigenvalue of the i-th measurement is expressed by the abovedescribed mathematical expression (2). Then, a value obtained bysubstituting the concrete i-th stabilized detected values (x_(i1),x_(i2), . . . , x_(in)) for the j-th principal component t_(ij) is ascore of the j-th principal component in the i-th measurement.Therefore, the score t_(j) of the j-th principal component is defined bythe above described mathematical expression (3), and the eigenvectorP_(j) of the j-th principal component is defined by the above describedmathematical expression (4). The t_(j) is a score denoting therelationship between measured values, and P_(j) is an eigenvectordenoting weights between measured values. Using a matrix X and theeigenvector P_(j), the score t_(j) of the j-th principal component isexpressed by the above described mathematical expression (5). Using thescores of principal components and their eigenvectors, the matrix X isexpressed by the above described mathematical expression (6):

[0194] Therefore, even if various kinds of measured data exist, theprincipal component analysis can integrate them into a small number ofstatistical data of, e.g. the first and second principal components andthe third principal component at most, and evaluate and grasp anoperation state only by examining the small number of statistical data.As described above, if the cumulative contribution of the eigenvalues ofthe first and second principal components exceeds 90%, the reliabilityof evaluation based on the first and second principal components isgenerally high. The first principal component denotes a direction inwhich measured data are most greatly distributed (varied) as describedabove, to be an index for comprehensively evaluating the operation stateof the processing system, to be suitable for the decision and evaluationof the variation of the operation state of the processing system withtime. The second principal component is distributed in a directionperpendicular to the first principal component, to be an index of aninstantaneous shift from a normal operation state, to be suitable forthe decision and evaluation of the sudden variation in operation state.

[0195] If the eigenvector, the first principal component scores and soforth are viewed, the first principal component can comprehensivelyevaluate which tendency exists in data. However, since the eigenvectorsin the first and second principal components are univocally determined,it is not possible to multilaterally grasp the state and variation ofthe individual measured data every measurement.

[0196] Therefore, in this preferred embodiment, as a technique fordetecting that the power application state of the high frequency power19 reaches a stable condition in accordance with the state in theprocessing vessel 11, a residual matrix E defined by the above describedmathematical expression (7) integrating the lower contribution principalcomponents of order higher than (k+1) is prepared (the components ofeach row correspond to the respective standardized measured values ofthe fundamental wave and its higher harmonic waves, and the componentsof each column correspond to the numbers of measurements). Then, thisresidual matrix E is adapted to the above described mathematicalexpression (6), the mathematical expression is expressed by the abovedescribed mathematical expression (8). In addition, the residual scoreof the residual matrix E of the reference processing system is derivedas a reference residual score Q₀. Then, on the basis of the residualscore Q₀, comparing with the reference processing system, it is detectedwhether the state of a comparative processing system to be monitoredreaches a stable condition after the system is started.

[0197] In general, the residual score Q_(i) is expressed as the productof a row vector e_(i) and its transposed vector e_(i) ^(T), and is thesum of squares of the respective residual components so as to be capableof being surely derived as a residual without setting plus componentsoff against minus components. Therefore, if the residual score Q₀ of thereference processing system every measurement is compared with theresidual score Q_(i) of the comparative processing system, it ispossible to determine whether the state of the comparative processingsystem reaches a stable condition. Then, when the residual score Q_(i)of the comparative processing system at a certain time deviates from theresidual score Q₀ of the reference processing system at that time, ifthe components of each row vector e_(i) of each row expressed by theabove described mathematical expression (9) in the residual matrix E areviewed, it is possible to determine which measured value has a greatdeviation at that time, so that it is possible to identify the cause ofabnormality.

[0198] That is, in order to detect the stable condition of thecomparative processing system, the residual score Q₀ of the residualmatrix E of the reference processing system is first derived. Then,constants, such as the residual score Q₀ and eigenvector obtained by thereference processing system, are set in the principal component analysisprogram for the comparative processing system, and the residual score Qis derived from electrical data measured in the comparative processingsystem on the set conditions. Then, the difference (deviation) betweenthe residual score Q of the comparative processing system and theresidual score Q₀ of the reference processing system is derived, and onthe basis of the difference (Q−Q₀) in residual score, it is determinedwhether the power application state of the high frequency source 19 inthe comparative processing system reaches a stable condition. That is,if the difference (Q−Q₀) in residual score is large, it shows that thecomparative processing system greatly deviates from the referenceprocessing system to be unstable, and if the difference (Q−Q₀) is small,it shows that the deviation of the comparative processing system fromthe reference processing system is small so that the state of thecomparative processing system is close to a stable condition. If theresidual score of the reference processing system is set so that Q₀=0,the residual score Q itself of the comparative processing system is thequantity of deviation from the reference level. It is assumed that thevalues of variables are calculated so that the mean value is 0.

[0199] On the basis of a method for monitoring an operation of aprocessing system according to the present invention, the followingstates A, B and processing conditions A, B were optionally combined toactually process wafers. The standardized measured values and residualscores Q of measured values V(f_(n)), I(f_(n)), P(f_(n)) and Z(f_(n)) offundamental waves and their higher harmonic waves during the processingare shown in FIGS. 17a through 20. In the principal component program ofthe comparative processing system, the results of principal componentanalysis obtained by the reference processing system are preset. Plotsin the respective figures denote mean values per wafer. It is assumedthat, with respect to the values of deposition in the followingprocessing conditions, a value on conditions that the quantity ofdeposition is small is 1, and conditions that the quantity of depositionis large are shown by relative values with respect to the conditionsthat the quantity of deposition is small.

[0200] I. State

[0201] State A: State that Processing Vessel has been evacuated for 12Hours

[0202] State B: State that Processing Vessel has been evacuated for 4days

[0203] II. Processing Conditions

[0204] Processing Condition A (Condition that the quantity of depositionis small)

[0205] Wafer Processing Time: 1 minute

[0206] High Frequency Power: 1700 W

[0207] Processing Vessel Pressure: 45 mTorr

[0208] Process Gases: C₄F₈=10 sccm, CO=50 sccm, Ar=200 sccm, O₂=5 sccm

[0209] Deposition: 1 (Relative Value)

[0210] Processing Condition B (Condition that the quantity of depositionis large)

[0211] Wafer Processing Time: 1 minute

[0212] High Frequency Power: 1500 W

[0213] Processing Vessel Pressure: 53 mTorr

[0214] Process Gases: C₄F₈=16 sccm, CO=300 sccm, Ar=400 sccm

[0215] Deposition: 1.95 (Relative Value)

[0216] First, referring to FIGS. 17a through 18 b, the difference instabilization due to the difference in processing condition aftermaintenance will be described below.

[0217] (1) State A+Processing Condition A (FIGS. 17a and 18 a)

[0218] After the state in the processing vessel 11 was lead to state A,the processing system was set to be in the processing condition A thatthe quantity of deposition was small. In this combination of the stateand the condition, wafers W carried in the processing vessel 11 wereprocessed. Immediately after the wafers were carried therein(immediately after the starting), the voltages, currents, phases andimpedances of the fundamental and higher harmonic waves were measuredevery about 0.2 seconds by means of the high frequency measuring device17, and the mean values of the respective measured values V(f_(n)),I(f_(n)), P(f_(n)) and Z(f_(n)) were derived every wafer. These meanvalues were converted into relative values with respect to thecorresponding values (reference values) of the reference processingsystem, and the variations thereof were shown in FIG. 17a.

[0219] According to the results shown in FIG. 17a, it can be seen thatthe respective measured values are gradually converging on the referencevalues (=1) immediately after the starting of processing, and reach thereference value levels in a region shown by ∘ in the figure to be in astable condition. However, the fluctuation in vertical directions isacknowledged after ∘. Also in the case shown in FIG. 17a, thedetermination of stable condition is easier than the conventional methodshown in FIGS. 23a and 23 b. On the other hand, the results of theresidual scores Q derived from the measured values by the method in thispreferred embodiment were shown in FIG. 18a. In FIG. 18a, the measuredvalues are integrated into one as the residual score Q, and thedetermination of deviation from the reference values is easier than thatin FIG. 17a, so that it can be determined that the stable condition isin the range of 100 to 120 processed wafers. Thereafter, it can be seenthat there is a tendency for the residual score Q to periodicallyslightly increase.

[0220] (2) State A+Processing Condition B (FIGS. 17b and 18 b)

[0221] After the state in the processing vessel 11 was lead to state Asimilar to (1), the processing system was set to be in the processingcondition B that the quantity of deposition was large unlike (1). Then,wafers W carried in the processing vessel 11 were processed. Measuredvalues were obtained immediately after the processing system was starteduntil the power application state of the high frequency power 19 wasstabilized. Thereafter, the relative values of the respective measuredvalues with respect to the reference values were derived similar to thecase of (1), and the results thereof were shown in FIG. 17b. Accordingto the results shown in FIG. 17b, although the respective measuredvalues more early approach a stable condition than the case of (1), theregion in which the respective measured values reach a stable conditionhaving a small amplitude is a region shown by ∘ to be substantially thesame as the case of (1). On the other hand, if the residual scores Q arederived by the method in this preferred embodiment, it can be seen fromFIG. 18b that the residual scores Q more early converge on the referencevalues than the case of (1) to reach the stable condition, so that it iseasy to determine the timing of reaching the stable condition. If theresidual scores as the standard for determining the stable condition arepreviously determined by using the reference processing system, it ispossible to surely determine the stable condition of the comparativeprocessing system.

[0222] Referring to FIGS. 19a, 19 b and 20, the difference instabilization due to the difference in state in the processing vesselafter maintenance will be described below.

[0223] (3) State A+Processing Condition A (FIGS. 19a and 20)

[0224] After the state in the processing vessel 11 was lead to state A,the processing system was set to be in the processing condition A thatthe quantity of deposition was small. Then, wafers W carried in theprocessing vessel 11 were processed. After measured values were obtainedimmediately after the processing system was started until the powerapplication state of the high frequency power 19 was stabilized.Thereafter, the relative values of the respective measured values withrespect to the reference values were derived similar to the case of (1),and the results thereof were shown in FIG. 19a. It can be seen from theresults shown in FIG. 19a that the respective measured values graduallyconverge on the reference values and slowly reach the stable condition.It can be determined that the measured values reach the stable conditionin a region shown by ∘ in which about 120 wafers were processed.However, thereafter, the measured values fluctuate in verticaldirections, so that it can be seen that it is difficult to determinestabilization. On the other hand, if residual scores Q were derived bythe method in this preferred embodiment, the state A shown in FIG. 20was obtained. It can be clearly seen from the results of the state Ashown in FIG. 20 that it unexpectedly takes a lot of time until theresidual score Q converges on the reference value unlike the resultsshown in FIG. 18a and is first in a stable condition in the vicinity ofthe region ∘ in which about 180 wafers are processed.

[0225] (4) State B+Processing Condition A (FIGS. 19b and 20)

[0226] After the state in the processing vessel 11 was lead to state B,the processing system was set to be in the processing condition A thatthe quantity of deposition was small, similar to the case of (3). Then,wafers W carried in the processing vessel 11 were processed. Aftermeasured values were obtained immediately after the processing systemwas started until the power application state of the high frequencypower 19 was stabilized. Thereafter, the relative values of therespective measured values with respect to the reference values werederived similar to the case of (1), and the results thereof were shownin FIG. 19b. It can be seen from the results shown in FIG. 19b that therespective measured values more early converge on the reference valuesthan the case of (3) and early reach the stable condition. In addition,residual scores Q were derived by the method in this preferredembodiment, the state B shown in FIG. 20 was obtained. It can be clearlyseen from the results of the state B shown in FIG. 20 that, although theresidual score Q early reaches the reference value, it fluctuates before100 wafers are processed, so that it is completely stabilized after 100wafers are processed.

[0227] As described above, according to this preferred embodiment, themeasured values V(f_(n)), I(f_(n)), P(f_(n)) and Z(f_(n)) of electricaldata, voltage, current, phase and impedance, of the fundamental andhigher harmonic waves of the stabilized processing system 1′ are usedfor previously carrying out a principal component analysis as areference to derive a reference residual score Q₀. Thereafter,electrical data are measured by the high frequency measuring device 17immediately after the comparative processing system 1′, which has beenprovided with maintenance and inspected, is started, and measured valuesV(f_(n)), I(f_(n)), P(f_(n)) and Z(f_(n)) are used for carrying out aprincipal component analysis to derive a residual score Q forcomparison. Then, the residual score Q for comparison is compared withthe residual score Q₀ for reference to detect the stable condition ofthe high frequency source 19 in the comparative processing system 1′after maintenance on the basis of the difference (Q−Q₀). Therefore, evenif vast numbers of measured values exist, if only the residual score Qobtained by integrating these data is compared with the reference value,the comparative processing system 1′, which has been provided withmaintenance and inspected, specifically the stable condition in theprocessing vessel 11 thereof, can be objectively and surely evaluatedand determined. According to this preferred embodiment, it is not onlypossible to evaluate and determine the timing of reaching the stablecondition, but it is also possible to evaluate and determine how to setprocessing conditions, such as the time to evaluate the processingvessel 11, to lead the state in the processing vessel 11 to the stablecondition.

Fourth Preferred Embodiment

[0228] A preferred embodiment of a method for detecting the abnormalityof a processing system will be described below.

[0229] The method for detecting the abnormality of the processing systemin this preferred embodiment is in common with the operation monitoringmethod in the above described third preferred embodiment at the pointthat the residual score Q in the principal component analysis is used.However, in this preferred embodiment, a normal processing system, i.e.a processing system which has no mounting errors for parts in theprocessing vessel 11 or high frequency source 19 and which is preciselyassembled in accordance with the design specification, is used as areference processing system. In this preferred embodiment, electricaldata of the fundamental wave and its higher harmonic waves are, ofcourse, measured at the stage that the power application state of thehigh frequency source 19 after the starting of the processing systemescapes from an unstable condition to reach a stable condition.

[0230] Therefore, also in this preferred embodiment similar to the abovedescribed preferred embodiment, the voltage, current, phase andimpedance of the fundamental wave and its higher harmonic waves withrespect to the reference processing system are intermittently measuredto obtain measured values V(f_(n)), I(f_(n)), P(f_(n)) and Z(f_(n)) ofthe respective frequencies to standardize these measured values. Then, aresidual score Q₀ defined by the above described mathematical expression(9) is previously derived with respect to the reference processingsystem. Constants, such as an eigenvector, obtained by the referenceprocessing system are set in the principal component analysis program ofthe comparative processing system, and a residual score Q is derivedfrom the electrical data of the comparative processing system on the setconditions. Then,the difference (deviation) between the residual scoreQ₀ of the reference processing system and the residual score Q of thecomparative processing system is obtained, and on the basis of thedifference (Q−Q₀) in residual score, it is determined whether thecomparative processing system is abnormal.

[0231] That is, if the difference (Q−Q₀) in residual score is large, itmeans that the comparative processing system has abnormality, such asmounting errors for parts of the processing vessel 11 or high frequencysource 19. On the other hand, if the difference (Q−Q₀) is below anallowable value, it is determined that the processing system is normal.If a certain residual score indicates a value different from anotherresidual score, residual components of a row having the different valueare noticed in the residual matrix E. For example, when the residualscore in the results of the i-th measurement is different from thereference residual score Q₀, if the residual component e_(ij) of e_(i)of the i-th row is viewed, it can be determined which variable (measuredvalue) contributes to the deviation of the residual score Q. Thus, it ispossible to classify causes of abnormality by associating the causes ofabnormality with variables (voltage, current etc. of fundamental andhigher harmonic waves) having a large residual.

[0232]FIG. 21 is a graph concretely showing the relationship betweenresidual scores Q and part-mounting errors. In FIG. 21, N1 and N2 denoteresidual scores of a normal processing system, state A denoting residualscores when a specific portion, state C denoting residual scores whenmissing a screw and a cover in a specific portion, state D denotingresidual scores when missing a screw in a portion different from stateA, state E denoting residual scores when missing a screw and a cover ina portion different from State C, state F denoting residual scores whena screw of a specific portion is loosened, and state G denoting residualscores when missing a specific part.

[0233] For example, with respect to the residual components of a rowshowing the residual scores in the state A of FIG. 21, it can be seenfrom FIG. 22a that the voltage V and impedance Z of the fundamental wave(f₀) particularly greatly deflect to the minus side and that the currentI of the third harmonic wave (f₃) particularly greatly deflects to theplus side. With respect to the state C in FIG. 21, it can be seen fromFIG. 22b that the voltage V and impedance Z of the fundamental waveparticularly greatly deflect to the minus side and that the phase P ofthe fundamental wave relatively greatly deflects to the plus side. Withrespect to the state G in FIG. 21, it can be seen from FIG. 22c that thecurrent I and phase P of the fundamental wave deflect to the minus sideand that the impedance Z of the fundamental wave particularly greatlydeflects to the plus side. Thus, it is possible to classify therelationship between the specific states (kinds and mounting portion ofrelated parts) and so forth causing abnormality, and components havinglarge residual scores. Therefore, by recognizing components having highcontributions to the residual scores by previously grasping thisrelationship, it is possible to determine which abnormality exists.

[0234] As described above, according to this preferred embodiment,measured data of the high frequency source 19 of a normal referenceprocessing system are previously used for carrying out a principalcomponent analysis to derive a residual score for reference. Then, aplurality of measured data obtained by measuring a plurality ofelectrical data of a comparative processing system are used for carryingout a principal component analysis to derive a residual score forreference. Then, the comparative residual score Q is compared with thereference residual score Q₀, so that the abnormality of the comparativeprocessing system can be detected from the difference (Q−Q₀). Thus, itis possible to surely detect the abnormality-due to part-mounting errorsand so forth without opening the processing system. It is also possibleto distinguish or classify the abnormalities, such as part mountingerrors, from the components of the residual matrix E in the processingsystem.

[0235] While the principal component analysis has been used as amultivariate analysis in the above described preferred embodiments, thepresent invention can be realized by using another multivariateanalysis, such as regression analysis. While the plasma processingsystem for etching a semiconductor wafer has been described as anexample, the present invention can be applied to another semiconductorproducing system or another general processing system.

1. An operation monitoring method for monitoring an operation of aprocessing system by utilizing a plurality of detected values asoperation data, the detected values being detected for every object tobe processed by means of a plurality of detectors provided in theprocessing system, wherein a multivariate analysis using the operationdata is carried out to evaluate an operation state of the processingsystem.
 2. An operation monitoring method as set forth in claim 1,wherein the multivariate analysis is a principal component analysis. 3.An operation monitoring method for monitoring an operation of a plasmaprocessing system by utilizing a plurality of detected values asoperation data, the detected values being detected for every object tobe processed by means of a plurality of detectors provided in the plasmaprocessing system, the method comprising the steps of: obtainingpreviously a plurality of operation data with respect to a plurality ofobjects to be processed as references; carrying out a principalcomponent analysis using the operation data; evaluating an operationstate of the plasma processing system on the basis of results of theprincipal component analysis.
 4. An operation monitoring method as setforth in claim 3, wherein a first principal component score is used asthe result of the principal component analysis.
 5. An operationmonitoring method as set forth in claim 4, wherein a variance value ofthe first principal component score is used for determining a timing ofstopping the operation.
 6. An operation monitoring method as set forthin claim 3, wherein a second principal component score is used as theresult of the principal component analysis.
 7. An operation monitoringmethod for monitoring an operation of a processing system by utilizing aplurality of detected values as operation data, the detected valuesbeing detected for every object to be processed by means of a pluralityof detectors provided in the processing system, the method comprisingthe steps of: dividing the operation data into relatively highcontribution principal components and low contribution principalcomponents; deriving a residual matrix of operation data belonging tothe low contribution principal components; and evaluating an operationstate of the processing system on the basis of a residual score obtainedby the residual matrix.
 8. A processing system evaluating method forevaluating a difference in characteristics between a plurality ofprocessing systems by utilizing a plurality of detected values asoperation data, the detected values being detected for every object tobe processed by means of a plurality of detectors provided in eachprocessing system, the method comprising the steps of: obtaining firstoperation data for each of a plurality of objects to be processed byusing a reference processing system; carrying out a multivariateanalysis using the first operation data; obtaining second operation datafor each of the objects to be processed by using a comparativeprocessing system to be compared with the reference processing system;obtaining an analyzed result wherein the second operation data areadapted to results of the multivariate analysis; and comparing resultsof analysis based on the first operation data with the results ofanalysis based on the second operation data to evaluate a difference inperformance between the processing systems.
 9. A processing systemevaluating method for evaluating a difference in characteristics betweena plurality of processing systems by utilizing a plurality of detectedvalues as operation data, the detected values being detected for everyobject to be processed by means of a plurality of detectors provided inthe processing systems, the method comprising the steps of: obtainingfirst operation data for each of the objects to be processed by using areference processing system; carrying out a principal component analysisusing the first operation data to derive a first residual matrix;obtaining second operation data for each of the objects to be processedby using a comparative processing system to be compared with thereference processing system; adapting the second operation data toresults of the principal component analysis to derive a second residualmatrix; and comparing the first residual matrix based on the firstoperation data with the second residual matrix based on the secondoperation data to evaluate a difference in performance between theprocessing systems.
 10. A processing system evaluating method as setforth in claim 9, wherein the comparison of the first and secondresidual matrixes with each other is carried out by using residualscores.
 11. A processing system monitoring method for measuring aplurality of electrical data of a high frequency source varying inaccordance with a state in a processing system, by means of a measuringdevice, when a high frequency power is applied to an electrode in aprocessing vessel from the high frequency source for processing anobject with plasma in the processing system, and for carrying out amultivariate analysis using the measured electrical data to detect apower application state of the high frequency source, the methodcomprising the steps of: measuring the electrical data as reference datawhen the power application state of the high frequency source isstabilized in accordance with the state in the processing vessel in areference processing system; carrying out a multivariate analysis forreference using the obtained reference data; measuring successively theelectrical data as comparative data in a comparative processing systemto be monitored, after the system is started; carrying out amultivariate analysis for comparison using the obtained comparativedata; and comparing a results of the multivariate analysis forcomparison with a result of the multivariate analysis for reference todetermine, on the basis of a difference therebetween, whether the powerapplication state of the high frequency source in the comparativeprocessing system reaches a stable condition in accordance with thestate in the processing vessel.
 12. An operation monitoring method asset forth in claim 11, wherein at least voltage, current, impedance andphase angle of a fundamental wave and higher harmonic waves are used asthe electrical data.
 13. An operation monitoring method as set forth inclaim 11, wherein the multivariate analysis is a principal componentanalysis.
 14. An operation monitoring method as set forth in claim 13,wherein a residual score is used as the result of the principalcomponent analysis.
 15. An operation monitoring method as set forth inclaim 14, wherein processing conditions and/or operating conditions inthe processing system are determined on the basis of a result ofcomparison of the residual scores with each other.
 16. An abnormalitydetecting method for detecting an abnormality of a processing system bymeasuring a plurality of electrical data of a high frequency sourcevarying in accordance with a state in the processing system, by means ofa measuring device, when a high frequency power is applied to anelectrode in a processing vessel from the high frequency source forprocessing an object with plasma in the processing system, and bycarrying out a multivariate analysis using the measured electrical datato detect a power application state of the high frequency source, themethod comprising the steps of: measuring the electrical data asreference data when the power application state of the high frequencysource is stabilized in accordance with the state in the processingvessel in a normal reference processing system; carrying out amultivariate analysis for reference using the obtained reference data;measuring the electrical data as comparative data in a comparativeprocessing system, the abnormality of which is to be detected; carryingout a multivariate analysis for comparison using the obtainedcomparative data; and comparing a result of the multivariate analysisfor comparison with a result of the multivariate analysis for referenceto detect the abnormality of the comparative processing system on thebasis of a difference therebetween.
 17. An abnormality detecting methodas set forth in claim 16, wherein at least voltage, current, impedanceand phase angle of a fundamental wave and higher harmonic waves are usedas the electrical data.
 18. An abnormality detecting method as set forthin claim 16, wherein the multivariate analysis is a principal componentanalysis.
 19. An abnormality detecting method as set forth in claim 18,wherein a residual score is used as the result of the principalcomponent analysis.
 20. An abnormality detecting method as set forth inclaim 18, wherein a cause of abnormality of the processing system isdistinguished on the basis of components of a residual matrix obtainedby the principal component analysis.